import numpy as np

def compute_error_for_line_given_points(b, w, points):
  totalError = 0
  for i in range(0, len(points)):
    x = points[i, 0]
    y = points[i, 1]
    totalError += (y - (w * x + b)) ** 2
  return totalError / float(len(points))

def step_gradient(b_current, w_current, points, learning_rate):
  b_gradient = 0
  w_gradient = 0
  N = float(len(points))
  for i in range(0, len(points)):
    x = points[i, 0]
    y = points[i, 1]
    b_gradient += -(2/N) * (y - ((w_current * x) + b_current))
    w_gradient += -(2/N) * x * (y - ((w_current * x) + b_current))
  new_b = b_current - (learning_rate * b_gradient)
  new_w = w_current - (learning_rate * w_gradient)
  return [new_b, new_w]


def gradient_descent_runner(points, starting_b, starting_w, learning_rate, num_iterations):
  b = starting_b
  w = starting_w
  for i in range(num_iterations):
    b, w = step_gradient(b, w, np.array(points), learning_rate)
  return [b, w]


def run():
  points = np.genfromtxt("data.csv", delimiter=",")
  learning_rate = 0.0001
  initial_b = 0
  initial_w = 0
  num_iterations = 1000
  print("Starting gradient descent at b = {0}, m = {1}, error = {2}"
  .format(initial_b, initial_w, compute_error_for_line_given_points(initial_b, initial_w, points))
  )
  print("Running...")
  [b, w] = gradient_descent_runner(points, initial_b, initial_w, learning_rate, num_iterations)
  print("After {0} iterations b = {1}, w = {2}, error = {3}".
  format(num_iterations, b, w, compute_error_for_line_given_points(b, w, points)))

if __name__ == '__main__':
    run()
